| | --- |
| | base_model: HuggingFaceTB/SmolLM2-135M |
| | library_name: transformers |
| | model_name: SmolLM2-FT-MyDataset |
| | tags: |
| | - generated_from_trainer |
| | - smol-course |
| | - module_1 |
| | - trl |
| | - sft |
| | licence: license |
| | --- |
| | |
| | # Model Card for SmolLM2-FT-MyDataset |
| |
|
| | This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M). |
| | It has been trained using [TRL](https://github.com/huggingface/trl). |
| |
|
| | ## Quick start |
| |
|
| | ```python |
| | from transformers import pipeline |
| | |
| | question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" |
| | generator = pipeline("text-generation", model="Aaron43/SmolLM2-FT-MyDataset", device="cuda") |
| | output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] |
| | print(output["generated_text"]) |
| | ``` |
| |
|
| | ## Training procedure |
| |
|
| | [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/zil105-ucla/huggingface/runs/91q5wcag) |
| |
|
| |
|
| | This model was trained with SFT. |
| |
|
| | ### Framework versions |
| |
|
| | - TRL: 0.13.0 |
| | - Transformers: 4.46.3 |
| | - Pytorch: 2.5.1+cu121 |
| | - Datasets: 3.2.0 |
| | - Tokenizers: 0.20.3 |
| |
|
| | ## Citations |
| |
|
| |
|
| |
|
| | Cite TRL as: |
| | |
| | ```bibtex |
| | @misc{vonwerra2022trl, |
| | title = {{TRL: Transformer Reinforcement Learning}}, |
| | author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, |
| | year = 2020, |
| | journal = {GitHub repository}, |
| | publisher = {GitHub}, |
| | howpublished = {\url{https://github.com/huggingface/trl}} |
| | } |
| | ``` |
